high performance computing on graphics processing units: hgpu.org

hgpu.org » CUDA

Multi-thread implementations of the lattice Boltzmann method on non-uniform grids for CPUs and GPUs

M. Schonherr, K. Kucher, M. Geier, M. Stiebler, S. Freudiger, M. Krafczyk

Tags: CUDA, Lattice Boltzmann model, Mathematics, nVidia

June 7, 2011 by hgpu

Massively Parallel Network Coding on GPUs

Xiaowen Chu, Kaiyong Zhao, Mea Wang

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Networks, nVidia, nVidia GeForce GTX 260

June 6, 2011 by hgpu

Accelerating biomedical signal processing algorithms with parallel programming on graphic processor units

Evdokimos I. Konstantinidis, Christos A. Frantzidis, Lazaros Tzimkas, Costas Pappas, Panagiotis D. Bamidis

View

Download (PDF)

Tags: CUDA, Medicine, nVidia, nVidia GeForce 8600 GT, nVidia GeForce 9600 GT, nVidia GeForce GTS 250, Signal processing

June 6, 2011 by hgpu

Exploiting Computing Power on Graphics Processing Unit

Ziyi Liu, Wenjing Ma

Tags: Algorithms, Computer science, CUDA, Memory model, nVidia, Performance

June 5, 2011 by hgpu

Large neighborhood local search optimization on graphics processing units

The Van Luong, Nouredine Melab, El-Ghazali Talbi

View

Download (PDF)

Tags: Computer science, CUDA, Metaheuristics, nVidia, nVidia GeForce GTX 280, Search

June 5, 2011 by hgpu

Interective Point Clouds Fairing on Many-Core System

Tang Jie, Wu Gangshan, Xu Bo, Gong Zhongliang

Tags: Computer science, CUDA, Nearest neighbour, nVidia

June 5, 2011 by hgpu

Implementation of association rule mining using CUDA

S.H. Adil, S. Qamar

Tags: Computer science, CUDA, Data mining, nVidia, nVidia GeForce GTX 200

June 5, 2011 by hgpu

Efficient Embarrassingly Parallel on Graphics Processor Unit

Chunye Gong, Jie Liu, Jin Qin, Qingfeng Hu, Zhenghu Gong

Tags: Benchmarking, Computer science, CUDA, nVidia, Performance, Pseudo-random number generators, Tesla T10

June 5, 2011 by hgpu

Accelerating Unstructured Mesh Computational Fluid Dynamics on the NVidia Tesla GPU Architecture

Graham Markall

View

Download (PDF)

Source codes

Tags: Conjugate gradient solver, CUDA, Fluid dynamics, nVidia, nVidia GeForce GTX 280, Package, Thesis

June 5, 2011 by hgpu

Performance Analysis of the OP2 Framework on Many-core Architectures

M.B. Giles, G.R. Mudalige, Z. Sharif, G. Markall and P.H.J Kelly

View

Download (PDF)

Source codes

Tags: CUDA, Fluid dynamics, GPU cluster, nVidia, nVidia GeForce GTX 260, OpenMP, Package, Performance, Tesla C2050

June 5, 2011 by hgpu

A framework for parallel unstructured grid applications on GPUs

Mike Giles

View

Download (PDF)

Source codes

Tags: Code generation, Computer science, CUDA, GPU cluster, MPI, nVidia, OpenCL, OpenMP, Package, PDEs, Presentation

June 5, 2011 by hgpu

QR Factorization on a Multicore Node Enhanced with Multiple GPU Accelerators

Emmanuel Agullo, Cedric Augonnet, Jack Dongarra, Mathieu Faverge, Hatem Ltaief, Samuel Thibault, Stanimire Tomov

View

Download (PDF)

Tags: Computer science, CUDA, Factorization, GPU cluster, Linear Algebra, nVidia, nVidia Quadro FX 5800, Tesla S1070

June 5, 2011 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

CuPBoP: Making CUDA a Portable Language

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Multi-thread implementations of the lattice Boltzmann method on non-uniform grids for CPUs and GPUs

Massively Parallel Network Coding on GPUs

Accelerating biomedical signal processing algorithms with parallel programming on graphic processor units

Exploiting Computing Power on Graphics Processing Unit

Large neighborhood local search optimization on graphics processing units

Interective Point Clouds Fairing on Many-Core System

Implementation of association rule mining using CUDA

Efficient Embarrassingly Parallel on Graphics Processor Unit

Accelerating Unstructured Mesh Computational Fluid Dynamics on the NVidia Tesla GPU Architecture

Performance Analysis of the OP2 Framework on Many-core Architectures

A framework for parallel unstructured grid applications on GPUs

QR Factorization on a Multicore Node Enhanced with Multiple GPU Accelerators

Recent source codes

CuPBoP-AMD: Extending CUDA to AMD Platforms

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

ROCm's implementation of Gromacs

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

Most viewed papers (last 30 days)